- Do not subscribe to every service at once. Most free credits expire quickly. Test them one by one and move to the next when your current credits run out.
- Register, verify your account, and claim your credits. Follow each platform’s documentation to connect your coding tools.
- Keep your API keys private. Do not share them publicly. (save or star this gist for future update)
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isdaviddong
/ index.html
Last active
August 18, 2026 14:00
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| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title></title> | |
| <meta charset="utf-8" /> | |
| <script src="Scripts/jquery-1.9.1.min.js"></script> | |
| <link href="Content/bootstrap.min.css" rel="stylesheet" /> | |
| <script src="Scripts/isRockFx.js"></script> | |
| <script> | |
| $(function () { |
almahmudbd
/ free-ai-api.md
Last active
August 18, 2026 14:00
Free Ai Api keys and credits for agentic coding.
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
Index Terms. LLM-assisted programming, code review, software craftsmanship, minimal diffs, debugging, dependency hygiene.
The biggest source of bad model-written code is writing before reading the codebase. Read the files you are about to touch; read, not skim. Copy the patterns that already exist, and check the imports to see what the project actually depends on, so you do not reach for axios where everything is fetch. When you cannot find a pattern, ask instead of guessing.
Figure out what you are doing before you type. State your assumptions ("add authentication" is five different things, so name the one you picked) and name the tradeoffs. If something is genuinely confusing, stop and ask rather than filling the gap with plausible-looking code; that is exactly the code that passes a casual review and fails when it matters.
thomasbrandon
/ PyTorch-Resample.ipynb
Last active
August 18, 2026 13:55
PyTorch implementation of scipy.signal.resample and scipy.signal.resample_poly
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| <?xml version="1.0" encoding="utf-8"?> | |
| <configuration> | |
| <system.diagnostics> | |
| <trace autoflush="true" /> | |
| <sources> | |
| <source name="System.Net" switchValue="Verbose"> | |
| <listeners> | |
| <add name="netlog" /> | |
| </listeners> |
duyfken
/ miui-fastboot-howto.rst
Last active
August 18, 2026 13:53
— forked from j-jith/miui-fastboot-howto.rst
How to flash MIUI Fastboot ROM from Linux
- Download the fastboot ROM suitable for your device from the XM Firmware Updater and extract the downloaded archive
- Download and extract the official Android SDK Platform Tools or install them using your package manager of choice.
- If you are using the official platform tools, make sure
adbandfastboot(components of platform-tools) are in your path
- If you are using the official platform tools, make sure
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
jamiemccarthy
/ gist:c1fb26e781bdce4e3b43525e68b3fbeb
Created
August 18, 2026 13:36
Claude, don't narrate history
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| ## Don't narrate bug history in docs or comments | |
| When writing docs, skill instructions, code comments, or anything else the *next* reader will consult, **describe what the current code/instruction does — not the bug history that led to it.** | |
| **Why:** Future readers see only the current state. Multi-paragraph "this used to silently fail" / "the old form caused X" prose is dead weight to them — they aren't doing the old thing, they're just trying to understand the current one. It also tends to be flabby because the bug history is secondhand from a finding, not directly observed. | |
| **How to apply:** | |
| - When implementing a fix from a "here's what went wrong" finding, the doc/comment should describe the new behavior, not narrate the old bug. | |
| - One short note like "the previous form mis-handled X" is fine if it prevents a future regression. A multi-paragraph postmortem is not. | |
| - For deep gotcha rationale, put it in a troubleshooting reference (loaded only when the symptom appears) and leave the main skill/instructi |
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